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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 OF CLOUD-BASED CHATBOT APPLICATION AT PT. TRAVELOKA SINGAPORE USING THE AGILE METHOD Pratama, Yuda Adi; Kristiana, Titin
Jurnal Pilar Nusa Mandiri Vol 19 No 1 (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.v19i1.3055

The role of customer service will be good if it can serve all obstacles or difficulties users face directly and in real time. However, there are times when the number of customer service is not proportional to the number of users who make complaints to customer service, and often, users ask questions generally available on Frequent Ask Questions (FAQ), so there are limitations and are fewer responsive in serving user complaints. By developing a cloud computing-based Chatbot application, it is hoped that it will make it easier for customer service to handle recurring questions and increase response time to users in real-time. The development of this chatbot application uses the agile method with the scrum framework. Where in the development process carried out is divided into several phases called sprints. The development of this application was carried out in 3 sprints from the time the project was announced to completion.

DESIGN DECISION SUPPORT SYSTEM FOR A MARKETPLACE SELECTION USING THE ELIMINATION METHOD ET CHOIX TRADUISANT LA REALITE Ramadhan, Rizal Furqan; Eliyen, Kunti
Jurnal Pilar Nusa Mandiri Vol 19 No 1 (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.v19i1.4011

The marketplace phenomenon has become exciting, especially for the younger generation actively browsing the internet. In the past, humans had to meet when making buying and selling transactions. With the emergence of a marketplace, it was enough to use smartphone media to make buying and selling transactions. Along with the times, many developers have created marketplaces with different characteristics. So it is necessary to research to provide a good marketplace recommendation following the community's needs. The needs of the average community to fulfil their daily activities, especially in terms of clothing and electronic goods at affordable prices on the marketplace application. The computational method used in this study is the Elimination Et Choix Traduisant La Realite method based on the Decision Support System as a database controller. The research results obtained the highest score in the Shopee marketplace, namely 77.5, followed by the Tokopedia marketplace with a value of 71. Calculations in the ELECTRE method involve a set of concordance and discordance stages that make it different from other methods. The value for each alternative comes from a questionnaire filled out by 63 Z generations. Generation Z is considered a generation that is close to technology.

APPLICATION OF THE APRIORI ALGORITHM TO DETERMINE THE COMBINATION OF POVERTY INDICATORS Siswanti, Sri; Vulandari, Retno Tri; Kusumaningrum, Andriani; Setiyowati, Setiyowati
Jurnal Pilar Nusa Mandiri Vol 19 No 1 (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.v19i1.4161

Poverty is a society that has not been solved until now. The decline in poverty in Laweyan District from 2000 to 2013 was 5.71%, among the five lowest in the reduction in the percentage of poverty in Central Java Province. The problem of poverty is very complex, and the differences in regional characteristics, as well as the techniques used, also influence the indicators of the causes of poverty and the formulation of policies for poverty alleviation. This study uses Principal Component Analysis as part of data preprocessing, followed by applying association rules with the Apriori Algorithm to explore the relationship pattern of poverty indicators. Based on the research that has been conducted on the poverty dataset, which consists of 46 attributes, it is found that the attributes that have passed the preprocessing data are six attributes, namely the Poor Population, ADHB in the Communication Sector, ADHB in the Mining and Excavation Sector, ADHB in the Agriculture and Food Crops Sector, ADHB in the Plantation Sector. and unemployment. These six attributes are transformed into Ascending, Fixed, and Descending categorical data. The fuzzification process for the increase and decrease categories uses the shoulder-type triangle membership function. Applying the Apriori Algorithm to the poverty dataset with a minimum support of 0.4 and a minimum confidence of 0.8 produces 38 rules that show the relationship between indicators and poverty and 134 rules that show the relationship pattern between indicators.

Lexicon-Based and Naive Bayes Sentiment Analysis for Recommending the Best Marketplace Selection as a Marketing Strategy for MSMEs Hoiriyah, Hoiriyah; Mardiana, Helva; Walid, Miftahul; Darmawan, Aang Kisnu
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.v19i1.4176

MSMEs (micro, small, and medium enterprises) play an essential role in the Indonesian economy, contributing to 60% of the country's GDP (gross domestic product), creating jobs, and increasing non-oil and gas exports. However, MSMEs in Indonesia face various challenges, including access to technology, digital marketing tools, financial resources, limited market distribution, and low technological literacy. Marketplaces provide an essential marketing channel for MSMEs to increase their competitiveness and sales. Sentiment analysis can assist businesses in making informed decisions about which marketplace to use to increase customer satisfaction. Apart from the importance of the marketplace for MSMEs in Indonesia, research on sentiment analysis for marketplace recommendations is still minimal. Therefore, this study aims to analyze six popular marketplaces in Indonesia using Lexicon-based and naïve Bayes research methods to provide the best marketplace recommendations for MSME marketing. The results showed that Blibli.com had the highest accuracy, followed by Tokopedia, Tiktokshop, Lazada, Shopee, and Bukalapak. Blibli.com received positive reviews with 96.33%, followed by Tokopedia with 95.25%, Tiktokshop with 94.61%, and Lazada with the highest accuracy. 94.22%, Shopee 92.18%, and Bukalapak 89.57%. This research has two significant contributions. First, making a scientific contribution by applying a combination model of lexicon-based and naïve Bayes to analyze market sentiment in Indonesia Second, offering a practical contribution by providing recommendations to MSME actors and policymakers in choosing the best marketplace for MSMEs marketing purposes in Indonesia. By utilizing the recommended marketplace, MSMEs can optimize their marketing strategy and increase their competitiveness in the digital marketplace.

REVIEW OF MANAGEMENT INFORMATION SYSTEMS USING EXPERT SYSTEMS IN INTERNAL CONTROL FOR INDONESIA'S BUREAUCRATIC REFORM Sinulingga, Minan; Djati, Sundring Pantja; Thamrin, Suyono; Saragih, Herlina Juni Risma; Wijaya, Hendy Risdianto
Jurnal Pilar Nusa Mandiri Vol 19 No 1 (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.v19i2.4182

Technological developments are driving changes in bureaucratic reform, through Presidential Regulation No. 81 of 2020 which targets that by 2025 a system of oversight and accountability will be created to create a government with high integrity. Increasing SPBE (Electronic Based Government System) by implementing MIS (Management Information System) is one of the keys to achieving this target. This study aims to examine the use of MIS for internal control (SIMWAS) using an expert system, especially for internal audits associated with bureaucratic reform in Indonesia. This research method uses a Systematic Literature Review (SLR) to find suitable articles from various sources from 2015 to 2021 and conducts a Trend Analysis of the SLR results and a total of 31 articles to find research trends. The results of this study indicate that there is still no research related to supervision and internal auditors associated with bureaucratic reform and performance. Research on internal audits to improve performance using SIMWAS using an expert system has also not been done yet. This research can be developed using artificial intelligence to evaluate, predict and improve the performance of internal auditors that impact organizational performance. This study shows that the use of MIS for internal control using an expert system for internal audit still needs to be improved to support bureaucratic reform, especially the increase in SPBE.

SENTIMENT ANALYSIS ON LGBT ISSUES IN INDONESIA WITH LEXICON-BASED AND SUPPORT VECTOR MACHINE ALGORITHMS Hoiriyah, Hoiriyah; Qomariya, Nurul; Darmawan, Aang Kisnu; Walid, Miftahul; Efenie, Yuri
Jurnal Pilar Nusa Mandiri Vol 19 No 1 (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.v19i1.4183

Non-heterosexual sexual orientation (LGBT) behavior today is one of the most pervasive issues in Indonesian culture. Because of its domino effect on social stability and physical and mental health, the phenomenon known as lesbian, gay, bisexual, and transgender (LGBT) has always been under scrutiny. The development of LGBT people in Indonesia reflects cultural changes that concern many people. Freedom of speech for LGBT people on social media has many public implications. Observation of this phenomenon gives rise to views of anomalies and discrepancies that have drawn criticism. Various attempts have been made to prevent the movement of LGBT people. However, until now, many still debate the pros and cons of this LGBT movement. The lexicon-based method uses a support vector machine to classify public opinion in TikTok video comments about LGBT issues. The lexicon-based method is used as a weighting method, and the support vector machine method is used as a classification method. The results show that the highest gain in sentiment is neutral, with percentage values of 61%, 56%, 68%, 69%, and 63%. The second is positive sentiment, with percentage values of 27%, 27%, 20%, 20%, and 29%. The rest have negative sentiments. With a relatively high accuracy of the five data sets sequentially at 93%, 89%, 95%, 97%, and 91%. This shows that the majority of Indonesians prefer to ignore the issue.

HUMAN-COMPUTER INTERACTION (HCI) IN A WEB-BASED STUDENT LEARNING OUTCOME MONITORING SYSTEM USING USER CENTERED DESIGN (UCD) Laraswati, Dewi; Indarti, Indarti; Wibowo, Agung
Jurnal Pilar Nusa Mandiri Vol 19 No 1 (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.v19i2.4148

Student learning outcomes in the learning process at school are usually expressed in the form of scores. This learning result is not only useful as feedback for students but also useful for parents and teachers. For parents, student learning outcomes are used as a reference to provide assistance in the form of attention, guidance, motivation, and others. As for teachers, learning outcomes are used as a benchmark to analyze the success of the teaching process that has been passed. Human Computer Interaction (HCI) is a scientific discipline that examines communication or interaction between users and systems. It was concluded that 73% of parents/guardians agreed to use the Student Learning Outcomes Monitoring System in the form of a student learning monitoring application to monitor children's learning progress at school.

The Prediction Of Product Sales Level Using K-Nearest Neighbor and Naive Bayes Algorithms (Case Study : PT Kotamas Bali) Miha Djami, Aris Setiawan; Utami, Nengah Widya; Paramitha, A. A. Istri Ita
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.v19i2.4420

PT Kotamas Bali is a company that operates in tableware and kitchenware, where every sale is sold at various counters. Product sales are permanently printed and entered in sales reports, and there are problems such as the fact that the product is sold very much and it is difficult to see the rate of sale of most products, lots, and not lots. Then, to see the level of product sales, it is necessary to use data mining techniques with the method of Knowledge Discovery in Databases to predict the purchase rate of products using the two algorithms K-Nearest Neighbor and Naïve Bayes. The purpose of this research is so that PT Kotamas Bali can see the sales rate of each product sold so that there is no accumulation of goods and more focus on the most marketed products. These two algorithms result in different accuracy on the 90:10 data split, where the K-Nearest Neighbor algorithm successfully predicted the sales rate of the product with a 99% accuracy rate and was categorized as an excellent classification. The Naïve Bayes algorithm failed to make predictions with an accuracy of only 54% and was classified as a failure classification. ROC performance results on the K-Nearest Neighbor algorithm with an AUC value of 99% and the Naïve Bayes algorithm with an AUC of 74%. K-Nearest Neighbor managed to obtain the highest accuracy, while the Naïve Bayes algorithm failed to conduct classification.

Implementation of Smarter Method for Prospective Student Council Selection System SMK Negeri 1 Rembang Sari, Bety Wulan; Prabowo, Donni; Lestari, Wahyu Puji
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.v19i2.4591

One of the schools that has attempted to make the student council active and the primary platform for student development to encourage student activities at school is SMK Negeri 1 Rembang. OSIS administrators can execute numerous labor programs in both academic and non-academic domains. Participants must pass several selection processes to join the SMK Negeri 1 Rembang OSIS board. This student council board's election procedure still employs manual methods. The selection procedure may take longer and allow for subjective evaluations depending on the number of candidates and the criteria used. As a result, it is essential to develop a decision support system (SPK) that uses Rank Order Centroid (ROC) weighting and the Simple Multi-Attribute Rating Technique Exploiting Rank (SMARTER) method to help choose student council administrators. The SMARTER technique addressed disproportionality because the weights assigned do not provide a hierarchy or order of importance between the current criteria and their sub-criteria. Based on the computation of the final value of the standards and sub-criteria on each alternative, the system produces results in the form of the biggest to most minor order. Blackbox testing of this program demonstrates that it can operate and be used at SMK N 1 Rembang both in terms of functionality and outcomes from the system.

Physical Violence Detection System to Prevent Student Mental Health Disorders Based on Deep Learning Putri, Sukmawati Anggraeni; Rifai, Achmad; Nawawi, Imam
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.v19i2.4600

Physical violence in the educational environment by students often occurs and leads to criminal acts. Apart from that, repeated acts of physical violence can be considered non-verbal bullying. This bullying can hurt the victim, causing physical disorders, mental health, impaired social relationships and decreased academic performance. However, monitoring activities against acts of violence currently being carried out have weaknesses, namely weak supervision by the school. A deep Learning-based physical violence detection system, namely LSTM Network, is the solution to this problem. In this research, we develop a Convolutional Neural Network to detect acts of violence. Convolutional Neural Network extracts features at the frame level from videos. At the frame level, the feature uses long short-term memory in the convolutional gate. Convolutional Neural Networks and convolutional short-term memory can capture local spatio-temporal features, enabling local video motion analysis. The performance of the proposed feature extraction pipeline is evaluated on standard benchmark datasets in terms of recognition accuracy. A comparison of the results obtained with state-of-the-art techniques reveals the promising capabilities of the proposed method for recognising violent videos. The model that has been trained and tested will be integrated into a violence detection system, which can provide ease and speed in detecting acts of violence that occur in the school environment.

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