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All Journal Jurnal Pengabdian UntukMu NegeRI Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Pilar Nusa Mandiri VOLT : Jurnal Ilmiah Pendidikan Teknik Elektro JITK (Jurnal Ilmu Pengetahuan dan Komputer) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Riset Informatika Journal of Information System, Applied, Management, Accounting and Research Jurnal AbdiMas Nusa Mandiri JPM: JURNAL PENGABDIAN MASYARAKAT Abditeknika - Jurnal Pengabdian Kepada Masyarakat J-SAKTI (Jurnal Sains Komputer dan Informatika) Tridharmadimas: Jurnal Pengabdian Kepada Masyarakat Jayakarta SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Esensi Infokom: Jurnal Esensi Sistem Informasi dan Sistem Komputer PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Jurnal Pengabdian Kreatif Cemerlang Indonesia Vertex Repeater: Publikasi Teknik Informatika dan Jaringan
Tuslaela
Universitas Nusa Mandiri
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Application of the Hungarian Method and Software Quality Management (QM) Testing in Determining Optimal Wage Costs at OneTop Frozen Food Stores Rusdiansyah, Rusdiansyah; Handrianto, Yopi; Supendar, Hendra; Tuslaela, Tuslaela
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 2 (2022): Articles Research Volume 6 Issue 2, April 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i2.11330

Abstract

Linear programming that can be found in life around is an assignment problem. Common assignment problems include n tasks that must be assigned to m workers assuming each worker has different competencies in completing each task. One of the methods in solving this problem is the Hungarian method. The purpose of this research is to optimize the assignment of employees by looking at the cost of daily wages. The problems that occur at the OneTop Frozen Store are the ineffectiveness of the work process and the swelling of operational costs, especially at work, Order Sorting, Packing, Labeling, Delivery with 4 workers. The application of the Hungarian method and testing of Software Quality Management obtains optimal wage costs so that operational costs can be reduced to a minimum without reducing the quality of service to consumers. Based on data processing in the Hungarian method, an assignment that is in accordance with the work in preparing Frozen Food orders at the OneTop Store can cost a daily wage of Rp.130,000 (one hundred and thirty thousand) to be the optimal wage cost per day. when compared with the previous calculation without using the Hungarian method by paying wages of Rp. 175,000 (one hundred and seventy five) per day. Based on the application of the Hungarian method, it is effective in determining an assignment and placement of workers so that they can work more effectively on a better and optimal Frozen Food ordering process.
Data Mining implementation on SMUN Scholarship recipient candidates using the C4.5 algorithm Rusdiansyah, Rusdiansyah; Supendar, Hendra; Suharyanti, Nining; Tuslaela, Tuslaela
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 4 (2022): Article Research: Volume 6 Number 4, October 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i4.11767

Abstract

This research is motivated by SMUN  previously only providing scholarships for underprivileged students, while for students who excel, the method is not yet, therefore, research by implementing data mining with the C4.5 algorithm method so that in the future, if the scholarship program is for outstanding students already exists, then SMUN  can immediately apply it. The sample data are 33 scholarship recipients with subjects that have been determined based on the report card scores of prospective scholarship recipients. The selection results for receiving scholarships to outstanding students so far have only been calculated in general terms, the possibility of incorrect results will cause losses for students who have good achievements. As a result, there are students who should get the scholarship rights and do not receive it. The problem of this research is how to make it easier to analyze students who need and are entitled to receive scholarships, how to make it easier to select and determine scholarship recipients. The research uses the C4.5 algorithm to overcome these problems. With the C4.5 algorithm, the percentage accuracy value can be calculated which is used to determine whether the student is entitled to receive a scholarship or not. The data collection technique in this study is to use student report cards. classification value in determining students who get scholarships with the highest accuracy of 81.81%. The accuracy value was obtained by experimenting with the testing process with the number of student data as many as 33 students. It is hoped that it will make it easier to receive scholarships for students who excel academically in school.
Application of the Naïve Bayes Algorithm in Determining Sales Of The Month Supendar, Hendra; Rusdiansyah , Rusdiansyah; Suharyanti, Nining; Tuslaela
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12293

Abstract

One important factor for creating a healthy and growing company is the existence of sales rewards for employees to achieve sales targets every month. Assessing employees is not an easy thing when there are so many employees. This will make the assessment team have to look at the criteria carefully and carefully. Data manipulation can occur because it is difficult to make decisions with such large criteria and data without automated data mining. As a result, the company will not get competitive human resources. Sales targets are one of the keys to sales success because with sales targets, the sales prediction value can be used as a guide as a reference in determining product sales. One way to make better sales predictions is by utilizing data mining processing using the Naive Bayes algorithm. The Naive Bayes algorithm calculates the probability value of each of the attributes examined including attendance, sales targets and sales returns. Research with employee absence criteria, monthly sales and monthly sales invoice returns. From the results of the research that has been done, it can be concluded that the application of the Naive Bayes classifier method to the target data set for sales of goods achieves an optimization level of 95.78%, with attendance criteria greatly affecting employee performance so that product sales targets each month can be achieved
Implementation of K-Means Clustering in Food Security by Regency in East Java Province in 2022 Tuslaela, Tuslaela; Rusdiansyah, Rusdiansyah; Supendar , Hendra; Suharyanti , Nining
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13169

Abstract

Food is the main need that society must fulfill. If food security is disrupted, it will have a negative impact on the nation's life. The agricultural sector has an important role in West Java Province. This province has a large area of agricultural land, so it has high potential to produce abundant agricultural production. However, knowing the adequate number of farmers is very important. Therefore, the implementation of K-Means Clustering can make a significant contribution to the East Java Provincial Agriculture Service in grouping farmers by district. To achieve optimal results, determining the best K value needs to be considered carefully. K-Means Cluster Analysis is a method of non-hierarchical Cluster Analysis that groups data into one or more groups. Data with the same characteristics is grouped into one cluster and data with different characteristics is grouped into another cluster. The data used in this research are land area and rice production in the Regency of East Java Province in 2022. Based on the results of research with the object of Food Security, it can be concluded that, the results of the analysis of the application of manual data mining calculations in Excel Software using the K-Means Clustering method, resulted in two types of clustering in the form of C0, namely the Highest Land Area and Production group with 4 districts: Jember Regency, Ngawi Regency, Bojonegoro Regency and Lamongan Regency, for C1 clustering, namely the Lowest Land Area and Production group with 25 districts in East Java Province
Web Program Testing Using Selenium Python: Best Practices and Effective Approaches Rusdiansyah, Rusdiansyah; Suharyanti, Nining; Supendar, Hendra; Tuslaela, Tuslaela
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13569

Abstract

A critical component of software development is testing programs on websites to make sure they run, are secure, and meet user expectations. This article explores efficient methods and best practices for utilizing Selenium with the Python programming language to perform program testing on websites. This study shows the outcomes of Selenium WebDriver-based automated testing on two webpages: https://demoqa.com/text-box and https://demoqa.com/login. Testing was done to assess Selenium's dependability and performance when it comes to completing text fields and completing the login procedure. The goal of the study is to evaluate Selenium WebDriver's dependability and effectiveness in basic testing jobs while spotting any issues that may arise. Using the appropriate locator to fill in text boxes efficiently using Selenium WebDriver ensures that the operation proceeds without major hiccups. The effective completion of the login procedure on the https://demoqa.com/login page further demonstrates the dependability of Selenium WebDriver for handling increasingly complicated interactions, including login. According to the analysis's findings, Selenium WebDriver is a dependable and efficient solution for test automation that performs consistently and steadily under a range of conditions. The research's conclusions highlight how crucial it is to use automation technologies in order to ensure software quality and boost testing effectiveness. Software engineers can detect issues more rapidly and completely test the functionality of applications with Selenium WebDriver, which enhances the overall quality of software development
KOMPARASI METODE KLASIFIKASI PADA ANALISIS SENTIMEN USAHA WARALABA BERDASARKAN DATA TWITTER Mardiana, Tati; Syahreva, Hafiz; Tuslaela, Tuslaela
Jurnal Pilar Nusa Mandiri Vol 15 No 2 (2019): Pilar Nusa Mandiri : Journal of Computing and Information System Periode Septemb
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1039.919 KB) | DOI: 10.33480/pilar.v15i2.752

Abstract

Saat ini usaha waralaba di Indonesia memiliki daya tarik yang relatif tinggi. Namun, para pelaku usaha banyak juga yang mengalami kegagalan. Bagi seseorang yang ingin memulai usaha perlu mempertimbangkan sentimen masyarakat terhadap usaha waralaba. Meskipun demikian, tidak mudah untuk melakukan analisis sentimen karena banyaknya jumlah percakapan di Twitter terkait usaha waralaba dan tidak terstruktur. Tujuan penelitian ini adalah melakukan komparasi akurasi metode Neural Network, K-Nearest Neighbor, Naïve Bayes, Support Vector Machine, dan Decision Tree dalam mengekstraksi atribut pada dokumen atau teks yang berisi komentar untuk mengetahui ekspresi didalamnya dan mengklasifikasikan menjadi komentar positif dan negatif. Penelitian ini menggunakan data realtime dari tweets pada Twitter. Selanjutnya mengolah data tersebut dengan terlebih dulu membersihkannya dari noise dengan menggunakan Phyton. Hasil pengujian dengan confusion matrix diperoleh nilai akurasi Neural Network sebesar 83%, K-Nearest Neighbor sebesar 52%, Support Vector Machine sebesar 83%, dan Decision Tree sebesar 81%. Penelitian ini menunjukkan metode Support Vector Machine dan Neural Network paling baik untuk mengklasifikasikan komentar positif dan negatif terkait usaha waralaba.
DECISION SUPPORT SYSTEM SELECTION OF THE BEST ANDROID SMARTPHONE USING THE METHOD OF MOORA Ernawati, Siti; Al Hakim, Imam Taftazani; Tuslaela, Tuslaela
Jurnal Pilar Nusa Mandiri Vol 16 No 2 (2020): 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.v16i2.1667

Abstract

In the current conditions of each person must have a smartphone due to a lot of activities are done online. These activities can be in the form of learning, purchasing, transportation, and so forth. Smartphones offered currently have various specifications, sometimes prospective buyers feel confused to choose a smartphone as what they need. To overcome the problems in the decision of the selection of the best android smartphone that is with the decision support system using the method of Multi-Objective Optimization based on Ratio Analysis (MOORA). In this study, the data collected based on the 100 questionnaires that were distributed. The criteria used, namely random access memory (RAM), camera, price, storage capacity, battery life, and screen size. The results of the calculation obtained in this study determine each brand and type of smartphone the best android. Expected in this study can help prospective buyers who are confused in choosing the best android smartphone
Cluster Analysis of Food Social Assistance in DKI Jakarta: K-Means Approach to Identify Expenditure Patterns and Beneficiaries Suharyanti , Nining; Rusdiansyah, Rusdiansyah; Supendar, Hendra; Tuslaela, Tuslaela
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.14095

Abstract

This study aims to evaluate the effectiveness of the K-Means algorithm in grouping social assistance recipients in DKI Jakarta based on various demographic and economic factors, such as income, number of family members, and living conditions. The main objective of this study is to optimize resource allocation in social assistance programs by identifying different recipient clusters, so that aid distribution becomes more targeted. In this study, the K-Means algorithm was used with an optimal number of clusters of 3, and produced an accuracy rate of 85%, indicating that this algorithm is effective in grouping large-scale and complex data. However, there are challenges related to the sensitivity of K-Means to outliers and data imbalances that affect the results of the analysis. The results also show that areas such as Central Jakarta and South Jakarta receive more social assistance compared to other areas such as North Jakarta and East Jakarta, reflecting differences in needs in various regions. These findings emphasize the importance of selecting the right variables, such as access to health facilities and economic conditions, in producing more accurate groupings. Overall, this study provides valuable insights into efforts to optimize the distribution of social assistance in DKI Jakarta and recommends further research to address the limitations that exist in the use of the K-Means algorithm, especially in the context of data that is imbalanced or has large variations.
Analisis Pemilihan Siswa Untuk Jalur SNMPTN dengan Metode Weighted Product (WP) Dan Weighted Sum Model (WSM) Tuslaela, T; Nazarius, Jerry Kristian
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 1 (2021): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i1.305

Abstract

SNMPTN (State Higher Education Entrance National Selection) is one of the pathways to be able to enter State Universities, this can be proven by the number of students who are interested in being able to enter State Universities with this pathway. SMAN 2 Depok directs their students so that they are not wrong in choosing the chosen State University. But there are still many students who are still doubtful about which university they will choose according to their abilities. With this problem many students end up choosing universities that are not within their abilities. Therefore, by using a Decision Support System which makes decisions based on data held by students can help determine the best university for students. The method used is Weighted Product (WP) and Weighted Sum Model (WSM), the criteria used for this method use the results of semester 1 to 5 grades that have been achieved by students. Using these 2 methods will produce different values for each university and each method will produce different grades. With this, students of SMAN 2 Depok can consider which university they will choose.
Analisis Pemilihan Siswa Untuk Jalur SNMPTN dengan Metode Weighted Product (WP) Dan Weighted Sum Model (WSM) Tuslaela, T; Nazarius, Jerry Kristian
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 1 (2021): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i1.305

Abstract

SNMPTN (State Higher Education Entrance National Selection) is one of the pathways to be able to enter State Universities, this can be proven by the number of students who are interested in being able to enter State Universities with this pathway. SMAN 2 Depok directs their students so that they are not wrong in choosing the chosen State University. But there are still many students who are still doubtful about which university they will choose according to their abilities. With this problem many students end up choosing universities that are not within their abilities. Therefore, by using a Decision Support System which makes decisions based on data held by students can help determine the best university for students. The method used is Weighted Product (WP) and Weighted Sum Model (WSM), the criteria used for this method use the results of semester 1 to 5 grades that have been achieved by students. Using these 2 methods will produce different values for each university and each method will produce different grades. With this, students of SMAN 2 Depok can consider which university they will choose.
Co-Authors Abdul Rahman Kadafi Abdul Rahman Kadafi Abdul Rahman Khadafi Al Hakim, Imam Taftazani Anneke Indra Kirana Cahyani Budihartanti Cahyani Budihartanti Cahyani Budihartanti Cahyani Budihartanti D. Desmira Dannys Permadi Dinda Juliana Ega Maulana Ditama Eka Kusuma Pratama Elok Nur Aeni Embun Fajar Wati Enok Tuti Alawiah Ernawati, Siti Faqih Muhammad Farhan Rahmadhani Helina Apriyani Ihsanurrijal Muwahid Indah Purnamasari INDAH PURNAMASARI Indah Purnamasari Ishak Kholil Ishak Kholil Istikharoh, Istikharoh Kadafi, Abdul Rahman Laila Septiana Nazarius, Jerry Kristian Ricardus Mba Dala Pati Rusdiansyah , Rusdiansyah Rusdiansyah Rusdiansyah Santoso Setiawan Siti Ernawati - STMIK Nusa Mandiri Sri Rusiyati Sri Rusiyati Suharyanti , Nining suharyanti, Nining Supendar , Hendra Syahreva, Hafiz Tati Mardiana Tati Mardiana, Tati Yopi Handrianto, Yopi Yoseph Adrianus Ino Yumi Novita Dewi Yunan Maulana Putra