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INDONESIA
MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer
Published by Universitas Bumigora
ISSN : 18584144     EISSN : 24769843     DOI : 10.30812/matrik
Core Subject : Science,
Computer Science & IT
MATRIK adalah salah satu Jurnal Ilmiah yang terdapat di Universitas Bumigora Mataram (eks STMIK Bumigora Mataram) yang dikelola dibawah Lembaga Penelitian dan Pengabadian kepada Masyarakat (LPPM). Jurnal ini bertujuan untuk memberikan wadah atau sarana publikasi bagi para dosen, peneliti dan praktisi baik di lingkungan internal maupun eksternal Universitas Bumigora Mataram. Jurnal MATRIK terbit 2 (dua) kali dalam 1 tahun pada periode Genap (Mei) dan Ganjil (Nopember).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 418 Documents
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Keputusan Pemberian Bantuan Sosial Program Keluarga Harapan Menggunakan Metode AHP dan SAW Aji Supriyanto; Jeffry Alfa Razaq; Purwatiningtyas Purwatiningtyas; Agus Ariyanto
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 3 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i3.1806

Abstract

Dalam rangka meningkatkan kesejahteraan masyarakat miskin, pemerintah memberikan berbagai macam bantuan sosial salah satunya Program Keluarga Harapan (PKH). Permasalahan utamanya adalah masih sering terjadi tidak tepat sasaran penerima PKH. Penyebab utama salah satunya adalah ketika petugas melakukan pendataan warga masih kesulitan dalam pengambilan keputusan untuk menentukan urutan kriteria calon penerima yang paling layak mendapatkan program tersebut. Penelitian ini bertujuan memberikan alternatif solusi dalam pengambilan keputusan pemberian bantuan sosial PKH dengan menggunakan metode AHP dan SAW. Metode AHP digunakan untuk pembobotan kriteria program yang telah ditentukan oleh pemerintah, sedangkan SAW digunakan untuk tahapan perankingan warga calon penerima program. Studi kasus yang digunakan adalah penentuan calon penerima PKH di Kelurahan Karanganyar Gunung Kota Semarang. Tahapan penelitian studi literatur terkait Sistem Pendukung Keputusan (SPK) dan bantuan sosial kemiskinan, Pengambilan data PKH, penerapan metode AHP dan SAW pada PKH, dan hasil preferensi. Hasil pengujian menunjukkan kriteria yang paling tinggi nilai bobotnya yaitu pada kriteria ibu hamil/menyusui dengan nilai 0,322, sedangkan nilai bobot terendah pada kriteria Anggota Rumah Tangga (ART) dengan nilai 0,018, dan ini menghasilkan nilai preferensi tertinggi yaitu 0,895. Hasil tersebut konsisten berdasarkan nilai Consistency Ratio (CR) = 0,0946, sehingga dapat ditentukan Ny Siswo Suwarno menduduki ranking tertinggi untuk mendapatkan Bansos.
Optimization of Performance Traditional Back-propagation with Cyclical Rule for Forecasting Model Anjar Wanto; Ni Luh Wiwik Sri Rahayu Ginantra; Surya Hendraputra; Ika Okta Kirana; Abdi Rahim Damanik
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i1.1826

Abstract

The traditional Back-propagation algorithm has several weaknesses, including long training times and significant iterations to achieve convergence. This study aims to optimize traditional Back-propagation using the cyclical rule method to cover these weaknesses. Optimization is done by changing the training function and standard Back-propagation parameters using the training function and cyclical rule parameters. After that, a comparison of the two results will be carried out. This study uses quantitative method of time-series data on coronavirus cases sourced from the Worldometer website, then analyzed using three forecasting models with five input layers, one hidden layer (5, 10, and 15 neurons) and one output layer. The results showed that the 5-10-1 model with the training function and cyclical rule parameters and the tansig and purelin activation functions could perform well in optimization, including faster training time and smaller iterations (epochs), MSE training performance, and better tests. Low and high accuracy (92%) with an error rate of 0.01. So it was concluded that the training function and cyclical rule parameters with the tansig and purelin activation functions were able to optimize the traditional Back-propagation method, and the 5-10-1 model could be used for forecasting active cases of the coronavirus in Asia
Development of OnlineWeb-Based New Student Graduation Application in Junior High School Jusmita Weriza; Ismail Husein; Noranizamardia Noranizamardia; M Fakhariza; Khairan Marzuki
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 3 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i3.1830

Abstract

The student graduation information system is integral to managing and carrying out school information system activities. Especially in Junior High Schools (SMP) today, many still have not used a website-based information system due to the new internet network entering this area. In meeting the admin’s need, in particular, to deal with the problem of passing the distribution of receiving information or announcements, documentation of activities, teaching materials, and registration of new student participants have not run optimally as expected. In the system development method in this research, the author uses the System Development Life Cycle (SDLC) with the Waterfall model approach, which consists of 5 stages: analysis, design, implementation, testing, and implementation. The system design tool uses the UML (Unified Modeling Language) method using use-case diagrams according to system requirements. In developing the SDLC method in this study, the authors used it until the design stage. The results obtained from this research are a Web-based application system for new students’ graduation. With this tool, several related files will be generated. This new system can improve the quality of further student admissions, graduation information and school information through the website.
Klasterisasi Lokasi Promosi PMB Dengan Fuzzy C-means Masa Pandemi Covid 19 Ni Gusti Ayu Dasriani; Mayadi Mayadi; Anthony Anggrawan
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 2 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i2.1832

Abstract

Pandemi Covid-19 saat ini merupakan bencana besar bagi global, covid 19 merupakan penyakit yang sangat merugikan dan memiliki dampak negative bagi global, resiko yang diakibatkan oleh Pandemi Covid-19 tidak hanya berpengaruh pada aspek kesehatan, tetapi juga berpengaruh pada berbagai lini kehidupan seperti dampak PHK dan merumahkan pekerja. Bukan hanya berdampak sektor ekonomi, transportasi dan pertanian, Pandemi Covid-19 ini sangat merugikan bagi dunia pendidikan. Selama pandemi covid 19 penurunan pendaftaran sangat berdampak terhadap dunia Pendidikan sehingga diperlukan strategi untuk bisa memancing minat calon mahasiswa untuk mendaftar. Berdasarkan permasalahan tersebut peneliti mencoba melakukan penelitian terkait strategi promosi di tengah pandemi covid 19 untuk menarik minat calon mahasiswa untuk mendaftar ke universitas. Metode yang digunakan menggunakan metode Fuzzy C-means dengan proses pembobotan menggunakan RFM (Recency, Frequency, Monetary). Dari hasil evaluasi dengan data pemetaan didapatkan peningkatan pendaftar dimana untuk tahun 2020 pendaftar sebanyak 365 dan untuk tahun 2021 mengalami peningkatan sebanyak 1169 pendaftar.
Recognize The Polarity of Hotel Reviews using Support Vector Machine Ni Wayan Sumartini Saraswati; I Gusti Ayu Agung Diatri Indradewi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i1.1848

Abstract

A brand is very dependent on consumer perceptions of the product or services. In assessing consumer perceptions of products and services, companies are often faced with data analysis problems. One of the data that is very useful to produce a picture of consumer perceptions of the products and services is review data. So that the company's ability to process review data means that the company has a picture of the strength of the brand it has. Some of the most popular machine learning algorithms for creating text classification models include the naive Bayes family of algorithms, support vector machines (SVM) and deep learning algorithms. In this research, SVM has been proven to be a reliable method in pattern recognition. In particular, this study aims to produce a model that can be used to classify the polarity of hotel reviews automatically. The experimental data comes from review data on hotels in Europe sourced from TripAdvisor with a total of 38000 reviews. We also measure the quality of the classification engine model. The test results of the SVM model built from hotel review data are quite good. The average accuracy of the classification engine is 92.48%. Because the recall and precision values ​​are balanced, the accuracy value is considered sufficient to describe the quality of the classification.
Implementation Cryptography and Access Control on IoT-Based Warehouse Inventory Management System Muhammad Yusuf; Arizal Arizal; Ira Rosianal Hikmah
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i1.1849

Abstract

Warehousing is a product storage management activity to ensure product availability, so inventory management is needed to oversee the movement of logistics and equipment. Some things need to be considered in the storage process, such as the suitability of the storage location, safe from theft, and safe from physical disturbances. Vulnerabilities can occur when unauthorized users find out information from the database regarding stored goods, so a security mechanism for the warehouse database is needed. In addition, proper identification needs to be made of someone trying to access the database. In this research, a Warehouse Inventory Management System (WIMS) was created by implementing the AES-128 cryptographic algorithm, which was built using ESP32 and Raspberry Pi 3 devices. Time Password (T-OTP). The results show that the built system can overcome inventory problems in conventional warehousing management systems and implement data security using the AES-128 algorithm. The application of two-factor authentication in the form of smartcards and T-OTP shows very good results in testing its accuracy to overcome the vulnerability of unauthorized access to the system database
Komparasi Ekstraksi Fitur dalam Klasifikasi Teks Multilabel Menggunakan Algoritma Machine Learning Lusiana Efrizoni; Sarjon Defit; Muhammad Tajuddin; Anthony Anggrawan
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 3 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i3.1851

Abstract

Ektraksi fitur dan algoritma klasifikasi teks merupakan bagian penting dari pekerjaan klasifikasi teks, yang memiliki dampak langsung pada efek klasifikasi teks. Algoritma machine learning tradisional seperti Na¨ıve Bayes, Support Vector Machines, Decision Tree, K-Nearest Neighbors, Random Forest, Logistic Regression telah berhasil dalam melakukan klasifikasi teks dengan ektraksi fitur i.e. Bag ofWord (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Documents to Vector (Doc2Vec), Word to Vector (word2Vec). Namun, bagaimana menggunakan vektor kata untuk merepresentasikan teks pada klasifikasi teks menggunakan algoritma machine learning dengan lebih baik selalumenjadi poin yang sulit dalam pekerjaan Natural Language Processing saat ini. Makalah ini bertujuan untuk membandingkan kinerja dari ekstraksi fitur seperti BoW, TF-IDF, Doc2Vec dan Word2Vec dalam melakukan klasifikasi teks dengan menggunakan algoritma machine learning. Dataset yang digunakan sebanyak 1000 sample yang berasal dari tribunnews.com dengan split data 50:50, 70:30, 80:20 dan 90:10. Hasil dari percobaan menunjukkan bahwa algoritma Na¨ıve Bayes memiliki akurasi tertinggi dengan menggunakan ekstraksi fitur TF-IDF sebesar 87% dan BoW sebesar 83%. Untuk ekstraksi fitur Doc2Vec, akurasi tertinggi pada algoritma SVM sebesar 81%. Sedangkan ekstraksi fitur Word2Vec dengan algoritma machine learning (i.e. i.e. Na¨ıve Bayes, Support Vector Machines, Decision Tree, K-Nearest Neighbors, Random Forest, Logistic Regression) memiliki akurasi model dibawah 50%. Hal ini menyatakan, bahwa Word2Vec kurang optimal digunakan bersama algoritma machine learning, khususnya pada dataset tribunnews.com.
The Improvement of Artificial Neural Network Accuracy Using Principle Component Analysis Approach Arief Hermawan; Adityo Permana Wibowo; Akmal Setiawan Wijaya
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i1.1880

Abstract

An important problem in a classification system is how to get good accuracy results. A way to increase the accuracy of a classifier system is to improve the number of input data attributes. Improving the number of input data attributes can be done using the Principal Component Analysis (PCA) method. The aim of this research is to reduce the number of input data attributes to increase the accuracy in a mushroom classification system. The research method used in this study started from collecting datasets from Kaggle.com related to mushroom-classification, then the data visualization process was carried out using pie charts then a dimension reduction process was carried out to reduce the number of variables using the PCA method. The next step is the training and testing of the artificial neural network. The architecture of artificial neural network used is backward error propagation with the number of hidden layers as much as 2 layers with the number of cells as many as 3 and 2. The training data used is 80%, while the testing data is 20%. Based on the test results, obtained an accuracy of 100% with 150,000 iterations and using 11 input variables from 22 existing input variables. By adding Principal Component Analysis part of the development that can improve the accuracy and performance of Artificial Neural Networks
Clustering Couples of Childbearing Age to Get Family Planning Counseling Using K-Means Method Yuniar Farida; Adam Fahmi Khariri; Dian Yuliati; Hani Khaulasari
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i1.1888

Abstract

Couples of Childbearing Age (CCA) in the Madiun Regency have increased in the last three years. It caused the population in Madiun to overgrow with the newborn, which implies the economic, social, and environmental aspects. This study aims to cluster villages in Madiun with CCA case studies instead of birth control participants who will give birth and want children to determine the priority of getting Family Planning (in Indonesia, namely Keluarga Berencana/KB) counseling. K-Means clustering is used in this study because it has a linear space of complexity that can be executed quickly and easily. The result of this study is four (4) CCA clusters. CCA cluster 1 is a very high level of giving birth and wanting children, consisting of 7 villages. CCA cluster 2 is a high level of giving birth and wanting children with 119 villages. CCA cluster 3 is a medium level of giving birth and wanting children in 50 villages, and CCA cluster 4 is a low level of giving birth and wanting children, including 34 villages. So, cluster 1, which includes seven villages, is the most prioritized to get Family Planning counseling because it is the CCA cluster with the most birthing rate and wants children. This research obtained a silhouette coefficient of 0.42, which belongs to the medium level.
Sentiment Analysis of Food Order Tweets to Find Out Demographic Customer Profile Using SVM Syahril Efendi; Poltak Sihombing
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 3 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i3.1898

Abstract

The use of online food ordering through food systems or applications continues to increase, requiring vendors to implement marketing and sales strategies through surveys, feedback. The problems that arise are building a system analysis model from a collection of tweets with hashtags or usernames for ordering food online . The Support Vector Machine (SVM) algorithm is used for text classification. Tweets are collected into data sets, training data, and testing data, then a classification model of the SVM Algorithm is built. Preprocessing data, tweets are cleansing, tokenized, and stopword remove. From the collected tweets, they are grouped into 10 variables to identify demographic profiles. The results of the analysis are classified as positive sentiments, namely residence, price range, using promos, paid types, halal food while negative sentiments are ethnicity, culture, vegetarianism, place. Classification accuracy is important to validate the results of the SVM model. From 500 train data tweet, the resulting classification is 66% positive sentiment and 34% negative sentiment. Overall accuracy model Linier SVM result 83.2% with accuracy 92.55%.

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