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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Journal of Nonformal Education Scientific Journal of Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Jurnal Ilmiah Media Sisfo JATI (Jurnal Mahasiswa Teknik Informatika) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF) Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Informatika dan Rekayasa Komputer Jurnal Manajemen Teknologi dan Sistem Informasi Jurnal Manajemen Sistem Informasi PROCEEDING OF INTERNATIONAL CONFERENCE ON BUSINESS MANAGEMENT AND ACCOUNTING
Jasmir, Jasmir
Teknik Informatika
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Social Sciences

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Sistem Pendukung Keputusan Pemilihan Jurusan Menggunakan Metode SAW Pada SMK Negeri 2 Sarolangun Rawal Dewa, Suhendra; Jasmir, Jasmir
Jurnal Manajemen Sistem Informasi Vol 8 No 1 (2023): MANAJEMEN SISTEM INFORMASI
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (689.993 KB) | DOI: 10.33998/jurnalmsi.2023.8.1.768

Abstract

The selection of majors at SMK Negeri 2 Sarolagun has difficulties with the new student registrationcommittee because the data for prospective students is quite a lot and the time available for processingdata is limited and processing values for each field is still using a manual system, using MicrosoftSuperior. For this reason, a support system for the selection of majors at SMK Negeri 2 Sarogun isneeded. Where the results and discussion in this Decision Support System are based on research thatis generally carried out in the process of selecting majors. The purpose of this research is to analyzeand design a decision support system for majors selection using the SAW (Simple Additive Weighting)method and using the UML (Unified Modeling Language) modeling tool. This research produces aprototype that can be implemented further into a system so as to produce a decision support system forthe selection ofmajors in SMK Negeri 2 Sarolangun.
Prediksi Masa Studi Mahasiswa Unama Jambi Menggunakan Metode Algoritma C4.5 Mega, Meyer; Jasmir, Jasmir
Jurnal Manajemen Sistem Informasi Vol 8 No 1 (2023): MANAJEMEN SISTEM INFORMASI
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1113.318 KB) | DOI: 10.33998/jurnalmsi.2023.8.1.770

Abstract

Every year the number of students at The University of Dynamics of the Nation jambi is always increasing but the students who graduate are different from the number of students who enter. Therefore, the author conducts a data mining analysis on student data so that it can be used by academic supervisors to find out the graduation status of students and as a warning so that students can graduate on time so as to reduce the number of delays in graduation. The author uses student data in 2016 and 2017 as training data and 2020 as testing data as many as 120 training data and 109 testing data and has carried out the data cleaning and attribute selection process using the forward selection method. In conducting the analysis, the author used the Tools Weka tool. The author uses the C4.5 algorithm method with 12 attributes, but there are 4 attributes that are most influential after selecting data using the forward selection method on WEKA. In this case, the author uses 4 test options, namely 5 Fold Cross Validation, 10 Fold Cross Validation, 70% Percentage Split, and 80% Percentage Split. The C4.5 Algorithm method produces the largest accuracy value in the training data, namely 5 Fold Cross Validation by 92.5% and in the testing data by 100%.
Penerapan Data Mining Untuk Memprediksi Prestasi Siswa SMA Pada Dinas Pendidikan Provinsi Jambi Lastari, Widya; Jasmir, Jasmir
Jurnal Manajemen Sistem Informasi Vol 8 No 2 (2023): MANAJEMEN SISTEM INFORMASI
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (597.612 KB)

Abstract

The implementation of education is one of the important efforts in improving the quality of students. With good education it will be useful in realizing the goals of students. This study utilizes data mining techniques using yahoo K-Nearest Neighbor (K-NN) to predict student achievement. The attributes used in this study are scores: Indonesian, Mathematics, English, Biology, Chemistry, Physics, Sociology, Economics, Geography and the target is the study of students. From the results of the study, the best results were at K = 3, the use of python sklean data mining got an accuracy value 61.9% and error MSE 0.8 by comparison Naive Baiyes with accuracy 58% and error MSE 0.41 , and for Rapid miners using KNN got value accucary51%.
Sistem Pendukung Keputusan Pembelian Mobil Dengan Metode AHP Pada Bintang Motor Muara Bungo Praptomo, Sidik; Jasmir, Jasmir
Jurnal Manajemen Sistem Informasi Vol 8 No 3 (2023): MANAJEMEN SISTEM INFORMASI
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jurnalmsi.2023.8.3.1479

Abstract

The high enthusiasm and interest of the public to own a private car provides a great opportunity for showroom business actors to improve service and sales. The absence of a system that can help provide recommendations to consumers makes it difficult for ordinary people to determine car purchases. In buying a car, consumers do not have a clear standard because there are so many aspects that are taken into consideration when making a purchase. This study aims to help consumers in choosing a car. With a decision support system, it is hoped that it can provide solutions to assist consumers in gett the appropriate car recommendations. The AHP method is a method whose main input is human perception so that the results of this method are close to desire, besides that the existence of a hierarchy allows solv complex or structured problems in sub-problems, and then compil them into a hierarchical form. A car purchase decision support system using the AHP method can help provide recommendations to consumers to make car buy decisions.
Sistem Pendukung Keputusan Pemberian Bantuan Bedah Rumah Dengan SAW Pada Dinas PUPR Provinsi Jambi Rahmawati, Sri; Jasmir, Jasmir
Jurnal Manajemen Sistem Informasi Vol 8 No 3 (2023): MANAJEMEN SISTEM INFORMASI
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jurnalmsi.2023.8.3.1486

Abstract

The increase in the number of house renovations at the Jambi Provincial PUPR Office from year to year has caused PPTK delays in determining prospective beneficiaries. For that we need a system that can support the decisions taken. The Decision Support System provides facilities to conduct analysis so that the decision-making process carried out by the PUPR Office of Jambi Province can be of higher quality. This study uses the SAW method and UML (Unified Modeling Language) modeling tools with the diagrams used include Use Case Diagrams, class diagrams, activity diagrams and this research produces a prototype that can be implemented further so as to produce a decision support system for home surgery assistance. at the PUPR Office of Jambi Province.
Sistem Pendukung Keputusan Penentuan Lokasi Alat Pengamatan BMKG Di Provinsi Jambi Margaretta S, Chinthya; Jasmir, Jasmir
Jurnal Manajemen Sistem Informasi Vol 8 No 4 (2023): MANAJEMEN SISTEM INFORMASI
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jurnalmsi.2023.8.4.1510

Abstract

Determining the remote site for each automatic equipment is one of the requirements for determining the location conditions in accordance with the operational conditions of weather equipment and seismic equipment. The Meteorology, Climatology and Geophysics Agency is a non-employee government agency that selects and determines a number of applicants who have qualified knowledge and skills and recruits new employees whose job is to provide information on meteorological, climatological and geophysica l activities. The system that can support the level of confidence in the location conditions according to the standards that must be achieved is using a decision support system for determining the location of BMKG observation tools, especially in Jambi Province. This system is designed using the mamdani fuzzy logic method where the AWS tool uses 6 (six) variables, namely location, location access, solar radiation potential, obstacles, communication networks, and security while the seismic tool uses 6 (six) variable, namely rock type, Tdom siteclass, location access, solar radiation potential, assessment results, and communication network. Mamdani fuzzy logic solves problems with intuitively approximated data with fuzzy logic controls. The results of the design of the decision support system prototype are in the form of survey locations shown on the map and the results of the decisions are in the form of feasible or infeasible conditions.
Implementasi Algoritma K-Means untuk Klasterisasi Penentuan Tempat Prakerin Hasanah Arif, Surya; Jasmir, Jasmir
Jurnal Manajemen Sistem Informasi Vol 9 No 1 (2024): MANAJEMEN SISTEM INFORMASI
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jurnalmsi.2024.9.1.1682

Abstract

Over time, the system of placement of internships in the Department of computer engineering and network (TKJ) vocational secondary school who runs the current gave rise to some of the problems faced by the Department as well as students. For the first system, the Department often difficulty in determining the location of internship students who place the grouping in accordance with knowledge and skills that students have. On the system of the two problems often found on the place of location of the internship the students find themselves because it does not fit with the majors TKJ. Therefore it is necessary a technique in determining the location of internship place groupings in accordance with scientific concentration and the ability of students to the party heading for easy placement of students in doing internships. Research undertaken aims to spot clusters academic competency-based apprenticeship that students have. The data used to make the Division of apprenticeship places cluster using data from 57 people grade 2 TKJ. To do this cluster will use the method of the K-Means algorithm. The cluster is formed into three clusters, namely: C0: Government Agencies; C1: Computer Store; and C2: provider network. Then the data will be in the analysis with the help of RapidMiner software to determine the result of the Division of apprenticeship places cluster. Obtained on the test results the overall data amounted to 57 the data on the cluster of Government Agencies: C0 found the data included as many as 29 students, in cluster C1: computer shop found the data included as many as 18 students, and in cluster C2: Member data Network Provider found a total of 10 students
Penerapan Data Mining dalam Pengelompokan Uang Kuliah Tunggal (UKT) Menggunakan Metode K-Means Pada Universitas Jambi Sophia, Aya; Jasmir, Jasmir
Jurnal Manajemen Sistem Informasi Vol 9 No 1 (2024): MANAJEMEN SISTEM INFORMASI
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jurnalmsi.2024.9.1.1692

Abstract

UKT is a single tuition fee borned by each undergraduate student at a state university in Indonesia, to be paid every semester. At Jambi University, 8 UKT groups apply to students based on the level of the student's economic condition. Determining the UKT group for new students manually is less effective because it really depends on the assessment of the assessor, the criteria for economic conditions are quite a lot, the potential for subjectivity, and the amount of data is quite large. This study grouped new students into 8 UKT groups by applying data mining using the k-Means method. The k-Means method performs clustering based on the similarity of data on student economic conditions. K-Means analysis was performed using Rapidminer and SPSS tools. The results show that there are 7 variables/attributes of economic condition parameters that can be used in k-means analysis, including: total parent/guardian income including additional income, parent/guardian employment, electricity bills, parent status, land and building tax bills, housing conditions, and the number of dependents who are still at school. The results show that between the interpretation of the k-means analysis and the real data, there was a similarity in determining the UKT group above 50% in both Rapidminer and SPSS. Thus it can be concluded that the k-Means method can be applied to support decision making in determining UKT groups for students.
Design of Purchase and Sales Information Systems at Bima Citra Stores Jasmir, Jasmir; Agustina, Agustina; Meisak, Despita
International Conference on Business Management and Accounting Vol 2 No 1 (2023): Proceeding of International Conference on Business Management and Accounting (Nov
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/icobima.v2i1.3533

Abstract

Bima Citra shop is a place of business in Tanjung Jabung Barat Regency, which is located on Jl.Imam Bonjol, Kuala Tungkal. Bima Citra shop is engaged in the sale of office stationery, school stationery, and so on whose data collection process is still using the manual method, namely still using handwritten notes for sales. With this system there are frequent recording and calculation errors that make the owners not know for sure whether they are experiencing profits or losses every day, besides that there are obstacles between physical stock data that are not recorded so that there is a difference in stock in the warehouse. This problem causes losses so that an information system is needed that provides convenience in providing information. This causes the owner to be overwhelmed in handling incoming and outgoing stock of goods because it is not recorded and does not recap so it is not known for sure how much stock is still available. Therefore, this study aims to design a purchasing and sales information system using the PHP programming language, MySQL DBMS as a database, waterfall system development method and system modeling in the form of use case diagrams, activity diagrams, and class diagrams. The result of this research is a system that helps manage business processes, such as processing data on purchases, sales, stock and is able to streamline time in reporting reports.
Experimental of information gain and AdaBoost feature for machine learning classifier in media social data Jasmir, Jasmir; Abidin, Dodo Zaenal; Fachruddin, Fachruddin; Riyadi, Willy
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1172-1181

Abstract

In this research, we use several machine learning methods and feature selection to process social media data, namely restaurant reviews. The selection feature used is a combination of information gain (IG) and adaptive boosting (AdaBoost) which is used to see its effect on the classification performance evaluation value of machine learning methods such as Naïve Bayes (NB), K-nearest neighbor (KNN), and random forest (RF) which is the aim of this research. NB is very simple and efficient and very sensitive to feature selection. Meanwhile, KNN is known for its weaknesses such as biased k values, overly complex computation, memory limitations, and ignoring irrelevant attributes. Then RF has weaknesses, including that the evaluation value can change significantly with only small data changes. In text classification, feature selection can improve the scalability, efficiency and accuracy of text classification. Based on tests that have been carried out on several machine learning methods and a combination of the two selection features, it was found that the best classifier is the RF algorithm. RF produces a significant increase in value after using the IG and AdaBoost features. Increased accuracy by 10%, precision by 12.43%, recall by 8.14% and F1-score by 10.37%. RF also produces even accuracy, precision, recall, and F1-score values after using IG and AdaBoost with an accuracy value of 84.5%; precision of 85.58%; recall was 86.36%; and F1-score was 85.97%.
Co-Authors Abdul Rahim Abidin, Dodo Zaenal Agus Siswanto Agus Siswanto Agustina Arvita, Yulia Ayu, Nur Trisna Fachruddin Fachruddin Faiza Rini Fatima Anggraini Fun, Chi-Win Gunardi Gunardi Hasanah Arif, Surya Hendri Hendri Heri Santoso Herti Yani, Herti Hidayatullah, Arief Irawan Irawan Juliana Juliana Jumairi, Nopri Lasmedi Afuan Lastari, Widya Margaretta S, Chinthya Mega, Meyer Meisak, Despita Muhammad Al-Aziz Sofyan Mulyadi Rusli Nurhadi OSCARIO OSCARIO Pahlevi B, M Riza Pardamean Simanjuntak, Daniel Sintong Pareza Alam Jusia, Pareza Alam Purnama, Benni Putri, Azhiah Raden Mohamad Herdian Bhakti Rasywir, Errisya Rawal Dewa, Suhendra Rohaini, Eni Rosario , Maria Sadikin , Ali Said, Rahaini Mohd Setiawan Assegaf Sharipuddin, Sharipuddin Sidik Praptomo Sophia, Aya Suryawahyuni Latief Takhir, Said Hambali Trivenna, Priskha WIJAYA, IBNU SANI WILLY RIYADI Yudha, M. Zahran Yudi Novianto Zudyanti Dwi Rahma Sari